{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeEval parameter optimization result analysis of extra experiments (2)\n",
    "\n",
    "Extra experiments and their reason:\n",
    "\n",
    "- Random Black Forest (RR): was missing in extra1 run, because of a configuration error\n",
    "- ARIMA: Inspect (originally fixed) parameter \"distance_metric\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "from timeeval import Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define data and results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available result directories:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/results/2021-10-11_optim-part4'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_shared-optim'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-06_optim-part1'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_default-params-1&2&3&4-merged'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-14_optim-extra'),\n",
       " PosixPath('/home/projects/akita/results/.ipynb_checkpoints'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-08_optim-part3'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-07_optim-part2'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-17_optim-extra2'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-12_optim-part5'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-12_optim-part6')]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selecting:\n",
      "Data path: /home/projects/akita/data/test-cases\n",
      "Result path: /home/projects/akita/results/2021-10-17_optim-extra2\n"
     ]
    }
   ],
   "source": [
    "# constants and configuration\n",
    "data_path = Path(\"/home/projects/akita/data\") / \"test-cases\"\n",
    "result_root_path = Path(\"/home/projects/akita/results\")\n",
    "experiment_result_folder = \"2021-10-17_optim-extra2\"\n",
    "\n",
    "# build paths\n",
    "result_paths = [d for d in result_root_path.iterdir() if d.is_dir()]\n",
    "print(\"Available result directories:\")\n",
    "display(result_paths)\n",
    "\n",
    "result_path = result_root_path / experiment_result_folder\n",
    "print(\"\\nSelecting:\")\n",
    "print(f\"Data path: {data_path.resolve()}\")\n",
    "print(f\"Result path: {result_path.resolve()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load results and dataset metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading results from /home/projects/akita/results/2021-10-17_optim-extra2\n"
     ]
    }
   ],
   "source": [
    "# load results\n",
    "print(f\"Reading results from {result_path.resolve()}\")\n",
    "df = pd.read_csv(result_path / \"results.csv\")\n",
    "\n",
    "# add dataset_name column\n",
    "df[\"dataset_name\"] = df[\"dataset\"].str.split(\".\").str[0]\n",
    "\n",
    "# load dataset metadata\n",
    "dmgr = Datasets(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract target optimized parameter names that were iterated in this run (per algorithm):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA ['distance_metric']\n",
      "Random Black Forest (RR) ['n_estimators', 'bootstrap', 'n_trees']\n"
     ]
    }
   ],
   "source": [
    "algo_param_mapping = {}\n",
    "algorithms = df[\"algorithm\"].unique()\n",
    "param_ignore_list = [\"max_anomaly_window_size\", \"anomaly_window_size\", \"neighbourhood_size\", \"window_size\", \"n_init_train\", \"embed_dim_range\"]\n",
    "\n",
    "for algo in algorithms:\n",
    "    param_sets = df.loc[df[\"algorithm\"] == algo, \"hyper_params\"].unique()\n",
    "    param_sets = [json.loads(ps) for ps in param_sets]\n",
    "    param_names = np.unique([name for ps in param_sets for name in ps if name not in param_ignore_list])\n",
    "    search_space = set()\n",
    "    for param_name in param_names:\n",
    "        values = []\n",
    "        for ps in param_sets:\n",
    "            try:\n",
    "                values.append(ps[param_name])\n",
    "            except:\n",
    "                pass\n",
    "        values = np.unique(values)\n",
    "        if values.shape[0] > 1:\n",
    "            search_space.add(param_name)\n",
    "    algo_param_mapping[algo] = list(search_space)\n",
    "\n",
    "for algo in algo_param_mapping:\n",
    "    print(algo, algo_param_mapping[algo])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract optimized parameters and their values (columns: optim_param_name and optim_param_value) for each experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hyper_params(algo):\n",
    "    param_names = algo_param_mapping[algo]\n",
    "    def extract(value):\n",
    "        params = json.loads(value)\n",
    "        result = \"\"\n",
    "        for name in param_names:\n",
    "            try:\n",
    "                value = params[name]\n",
    "                result += f\"{name}={value},\"\n",
    "            except KeyError:\n",
    "                pass\n",
    "        \n",
    "        if result == \"\":\n",
    "            return pd.Series([np.nan, np.nan], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "        elif len(param_names) == 1:\n",
    "            return pd.Series(result.rsplit(\",\", 1)[0].split(\"=\"), index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "        else:\n",
    "            return pd.Series([\"\", \"\".join(result.rsplit(\",\", 1))], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "    return extract\n",
    "\n",
    "df[[\"optim_param_name\", \"optim_param_value\"]] = \"\"\n",
    "for algo in algo_param_mapping:\n",
    "    df_algo = df.loc[df[\"algorithm\"] == algo]\n",
    "    df.loc[df_algo.index, [\"optim_param_name\", \"optim_param_value\"]] = df_algo[\"hyper_params\"].apply(extract_hyper_params(algo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scores_df(algorithm_name, dataset_id, optim_params, repetition=1):\n",
    "    params_id = df.loc[(df[\"algorithm\"] == algorithm_name) & (df[\"collection\"] == dataset_id[0]) & (df[\"dataset\"] == dataset_id[1]) & (df[\"optim_param_name\"] == optim_params[0]) & (df[\"optim_param_value\"] == optim_params[1]), \"hyper_params_id\"].item()\n",
    "    path = (\n",
    "        result_path /\n",
    "        algorithm_name /\n",
    "        params_id /\n",
    "        dataset_id[0] /\n",
    "        dataset_id[1] /\n",
    "        str(repetition) /\n",
    "        \"anomaly_scores.ts\"\n",
    "    )\n",
    "    return pd.read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define plotting functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_use_plotly = True\n",
    "try:\n",
    "    import plotly.offline\n",
    "except ImportError:\n",
    "    default_use_plotly = False\n",
    "\n",
    "def plot_scores(algorithm_name, dataset_name, use_plotly: bool = default_use_plotly, **kwargs):\n",
    "    if isinstance(algorithm_name, tuple):\n",
    "        algorithms = [algorithm_name]\n",
    "    elif not isinstance(algorithm_name, list):\n",
    "        raise ValueError(\"Please supply a tuple (algorithm_name, optim_param_name, optim_param_value) or a list thereof as first argument!\")\n",
    "    else:\n",
    "        algorithms = algorithm_name\n",
    "    # construct dataset ID\n",
    "    dataset_id = (\"GutenTAG\", f\"{dataset_name}.unsupervised\")\n",
    "\n",
    "    # load dataset details\n",
    "    df_dataset = dmgr.get_dataset_df(dataset_id)\n",
    "\n",
    "    # check if dataset is multivariate\n",
    "    dataset_dim = df.loc[df[\"dataset_name\"] == dataset_name, \"dataset_input_dimensionality\"].unique().item()\n",
    "    dataset_dim = dataset_dim.lower()\n",
    "    \n",
    "    auroc = {}\n",
    "    df_scores = pd.DataFrame(index=df_dataset.index)\n",
    "    skip_algos = []\n",
    "    algos = []\n",
    "    for algo, optim_param_name, optim_param_value in algorithms:\n",
    "        optim_params = f\"{optim_param_name}={optim_param_value}\"\n",
    "        algos.append((algo, optim_params))\n",
    "        # get algorithm metric results\n",
    "        try:\n",
    "            auroc[(algo, optim_params)] = df.loc[\n",
    "                (df[\"algorithm\"] == algo) & (df[\"dataset_name\"] == dataset_name) & (df[\"optim_param_name\"] == optim_param_name) & (df[\"optim_param_value\"] == optim_param_value),\n",
    "                \"ROC_AUC\"\n",
    "            ].item()\n",
    "        except ValueError:\n",
    "            warnings.warn(f\"No ROC_AUC score found! Probably {algo} with params {optim_params} was not executed on {dataset_name}.\")\n",
    "            auroc[(algo, optim_params)] = -1\n",
    "            skip_algos.append((algo, optim_params))\n",
    "            continue\n",
    "\n",
    "        # load scores\n",
    "        training_type = df.loc[df[\"algorithm\"] == algo, \"algo_training_type\"].values[0].lower().replace(\"_\", \"-\")\n",
    "        try:\n",
    "            df_scores[(algo, optim_params)] = load_scores_df(algo, (\"GutenTAG\", f\"{dataset_name}.{training_type}\"), (optim_param_name, optim_param_value)).iloc[:, 0]\n",
    "        except (ValueError, FileNotFoundError):\n",
    "            warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_name} with params {optim_params}.\")\n",
    "            df_scores[(algo, optim_params)] = np.nan\n",
    "            skip_algos.append((algo, optim_params))\n",
    "    algorithms = [a for a in algos if a not in skip_algos]\n",
    "\n",
    "    if use_plotly:\n",
    "        return plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "    else:\n",
    "        return plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "\n",
    "def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import plotly.offline as py\n",
    "    import plotly.graph_objects as go\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.express as px\n",
    "    from plotly.subplots import make_subplots\n",
    "\n",
    "    # Create plot\n",
    "    fig = make_subplots(2, 1)\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=f\"channel-{i}\"), 1, 1)\n",
    "    else:\n",
    "        fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name=\"timeseries\"), 1, 1)\n",
    "    fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset[\"is_anomaly\"], name=\"label\"), 2, 1)\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[item], name=f\"{algo}={auroc[item]:.4f} ({optim_params})\"), 2, 1)\n",
    "    fig.update_xaxes(matches=\"x\")\n",
    "    fig.update_layout(\n",
    "        title=f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\",\n",
    "        height=400\n",
    "    )\n",
    "    return py.iplot(fig)\n",
    "\n",
    "def plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # Create plot\n",
    "    fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8))\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            axs[0].plot(df_dataset.index, df_dataset.iloc[:, i], label=f\"channel-{i}\")\n",
    "    else:\n",
    "        axs[0].plot(df_dataset.index, df_dataset.iloc[:, 1], label=f\"timeseries\")\n",
    "    axs[1].plot(df_dataset.index, df_dataset[\"is_anomaly\"], label=\"label\")\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        axs[1].plot(df_scores.index, df_scores[item], label=f\"{algo}={auroc[item]:.4f} ({optim_params})\")\n",
    "    axs[0].legend()\n",
    "    axs[1].legend()\n",
    "    fig.suptitle(f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\")\n",
    "    fig.tight_layout()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PR_AUC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ROC_AUC</th>\n",
       "      <th>train_main_time</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>repetition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>optim_param_name</th>\n",
       "      <th>optim_param_value</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"18\" valign=\"top\">Random Black Forest (RR)</th>\n",
       "      <th rowspan=\"18\" valign=\"top\"></th>\n",
       "      <th>n_estimators=200,bootstrap=False,n_trees=10</th>\n",
       "      <td>0.567433</td>\n",
       "      <td>0.722157</td>\n",
       "      <td>0.668689</td>\n",
       "      <td>0.855922</td>\n",
       "      <td>0.903985</td>\n",
       "      <td>0.893457</td>\n",
       "      <td>592.900867</td>\n",
       "      <td>12.263722</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=True,n_trees=10</th>\n",
       "      <td>0.453341</td>\n",
       "      <td>0.656924</td>\n",
       "      <td>0.625191</td>\n",
       "      <td>0.821845</td>\n",
       "      <td>0.890477</td>\n",
       "      <td>0.871508</td>\n",
       "      <td>202.904011</td>\n",
       "      <td>9.182927</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=True,n_trees=10</th>\n",
       "      <td>0.457374</td>\n",
       "      <td>0.658582</td>\n",
       "      <td>0.628798</td>\n",
       "      <td>0.824355</td>\n",
       "      <td>0.890186</td>\n",
       "      <td>0.872921</td>\n",
       "      <td>371.430508</td>\n",
       "      <td>11.810795</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=10,bootstrap=True,n_trees=100</th>\n",
       "      <td>0.434542</td>\n",
       "      <td>0.649658</td>\n",
       "      <td>0.625823</td>\n",
       "      <td>0.825162</td>\n",
       "      <td>0.889673</td>\n",
       "      <td>0.871924</td>\n",
       "      <td>202.512872</td>\n",
       "      <td>8.796479</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=10,bootstrap=True,n_trees=200</th>\n",
       "      <td>0.434308</td>\n",
       "      <td>0.649504</td>\n",
       "      <td>0.624920</td>\n",
       "      <td>0.823742</td>\n",
       "      <td>0.889138</td>\n",
       "      <td>0.872064</td>\n",
       "      <td>390.331313</td>\n",
       "      <td>11.711196</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=False,n_trees=10</th>\n",
       "      <td>0.450179</td>\n",
       "      <td>0.656649</td>\n",
       "      <td>0.629158</td>\n",
       "      <td>0.812563</td>\n",
       "      <td>0.888855</td>\n",
       "      <td>0.870925</td>\n",
       "      <td>319.221750</td>\n",
       "      <td>9.705644</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=10,bootstrap=True,n_trees=10</th>\n",
       "      <td>0.435874</td>\n",
       "      <td>0.650336</td>\n",
       "      <td>0.615493</td>\n",
       "      <td>0.828276</td>\n",
       "      <td>0.888069</td>\n",
       "      <td>0.872081</td>\n",
       "      <td>29.630008</td>\n",
       "      <td>5.924876</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
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       "      <td>0.395472</td>\n",
       "      <td>0.642390</td>\n",
       "      <td>0.632714</td>\n",
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       "      <td>10</td>\n",
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       "      <td>0.631575</td>\n",
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       "      <td>323.007021</td>\n",
       "      <td>9.432247</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=10,bootstrap=False,n_trees=200</th>\n",
       "      <td>0.431431</td>\n",
       "      <td>0.568350</td>\n",
       "      <td>0.574051</td>\n",
       "      <td>0.800651</td>\n",
       "      <td>0.836222</td>\n",
       "      <td>0.837644</td>\n",
       "      <td>676.967207</td>\n",
       "      <td>12.128848</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=False,n_trees=100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=False,n_trees=200</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=True,n_trees=100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=True,n_trees=200</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=False,n_trees=100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=False,n_trees=200</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=True,n_trees=100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=True,n_trees=200</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">ARIMA</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">distance_metric</th>\n",
       "      <th>twed</th>\n",
       "      <td>0.028961</td>\n",
       "      <td>0.398863</td>\n",
       "      <td>0.346958</td>\n",
       "      <td>0.489981</td>\n",
       "      <td>0.824566</td>\n",
       "      <td>0.895639</td>\n",
       "      <td>NaN</td>\n",
       "      <td>783.581304</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euclidean</th>\n",
       "      <td>0.057625</td>\n",
       "      <td>0.405702</td>\n",
       "      <td>0.310566</td>\n",
       "      <td>0.493221</td>\n",
       "      <td>0.822603</td>\n",
       "      <td>0.883404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>785.055934</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>garch</th>\n",
       "      <td>0.025700</td>\n",
       "      <td>0.338990</td>\n",
       "      <td>0.320248</td>\n",
       "      <td>0.428535</td>\n",
       "      <td>0.801877</td>\n",
       "      <td>0.825859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>775.989576</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fourier</th>\n",
       "      <td>0.057153</td>\n",
       "      <td>0.353973</td>\n",
       "      <td>0.265896</td>\n",
       "      <td>0.543669</td>\n",
       "      <td>0.789106</td>\n",
       "      <td>0.848267</td>\n",
       "      <td>NaN</td>\n",
       "      <td>776.005241</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mahalanobis</th>\n",
       "      <td>0.013498</td>\n",
       "      <td>0.271808</td>\n",
       "      <td>0.184918</td>\n",
       "      <td>0.428803</td>\n",
       "      <td>0.749204</td>\n",
       "      <td>0.831612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>781.116264</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dtw</th>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.247100</td>\n",
       "      <td>0.240970</td>\n",
       "      <td>0.451673</td>\n",
       "      <td>0.743309</td>\n",
       "      <td>0.741703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>757.913729</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssa</th>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.212469</td>\n",
       "      <td>0.110486</td>\n",
       "      <td>0.296797</td>\n",
       "      <td>0.613737</td>\n",
       "      <td>0.711949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>774.143631</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edrs</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.382139</td>\n",
       "      <td>0.370515</td>\n",
       "      <td>0.307079</td>\n",
       "      <td>0.585320</td>\n",
       "      <td>0.569212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>819.568392</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                          PR_AUC  \\\n",
       "                                                                                             min   \n",
       "algorithm                optim_param_name optim_param_value                                        \n",
       "Random Black Forest (RR)                  n_estimators=200,bootstrap=False,n_trees=10   0.567433   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=10    0.453341   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=10    0.457374   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=100    0.434542   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=200    0.434308   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=10   0.450179   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=10     0.435874   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=10    0.395472   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=100   0.396960   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=200   0.431431   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200        NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200        NaN   \n",
       "ARIMA                    distance_metric  twed                                          0.028961   \n",
       "                                          euclidean                                     0.057625   \n",
       "                                          garch                                         0.025700   \n",
       "                                          fourier                                       0.057153   \n",
       "                                          mahalanobis                                   0.013498   \n",
       "                                          dtw                                           0.001008   \n",
       "                                          ssa                                           0.000710   \n",
       "                                          edrs                                          0.005000   \n",
       "\n",
       "                                                                                                  \\\n",
       "                                                                                            mean   \n",
       "algorithm                optim_param_name optim_param_value                                        \n",
       "Random Black Forest (RR)                  n_estimators=200,bootstrap=False,n_trees=10   0.722157   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=10    0.656924   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=10    0.658582   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=100    0.649658   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=200    0.649504   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=10   0.656649   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=10     0.650336   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=10    0.642390   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=100   0.642085   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=200   0.568350   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200        NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200        NaN   \n",
       "ARIMA                    distance_metric  twed                                          0.398863   \n",
       "                                          euclidean                                     0.405702   \n",
       "                                          garch                                         0.338990   \n",
       "                                          fourier                                       0.353973   \n",
       "                                          mahalanobis                                   0.271808   \n",
       "                                          dtw                                           0.247100   \n",
       "                                          ssa                                           0.212469   \n",
       "                                          edrs                                          0.382139   \n",
       "\n",
       "                                                                                                  \\\n",
       "                                                                                          median   \n",
       "algorithm                optim_param_name optim_param_value                                        \n",
       "Random Black Forest (RR)                  n_estimators=200,bootstrap=False,n_trees=10   0.668689   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=10    0.625191   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=10    0.628798   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=100    0.625823   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=200    0.624920   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=10   0.629158   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=10     0.615493   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=10    0.632714   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=100   0.631575   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=200   0.574051   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200        NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200        NaN   \n",
       "ARIMA                    distance_metric  twed                                          0.346958   \n",
       "                                          euclidean                                     0.310566   \n",
       "                                          garch                                         0.320248   \n",
       "                                          fourier                                       0.265896   \n",
       "                                          mahalanobis                                   0.184918   \n",
       "                                          dtw                                           0.240970   \n",
       "                                          ssa                                           0.110486   \n",
       "                                          edrs                                          0.370515   \n",
       "\n",
       "                                                                                         ROC_AUC  \\\n",
       "                                                                                             min   \n",
       "algorithm                optim_param_name optim_param_value                                        \n",
       "Random Black Forest (RR)                  n_estimators=200,bootstrap=False,n_trees=10   0.855922   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=10    0.821845   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=10    0.824355   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=100    0.825162   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=200    0.823742   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=10   0.812563   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=10     0.828276   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=10    0.799855   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=100   0.800889   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=200   0.800651   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200        NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200        NaN   \n",
       "ARIMA                    distance_metric  twed                                          0.489981   \n",
       "                                          euclidean                                     0.493221   \n",
       "                                          garch                                         0.428535   \n",
       "                                          fourier                                       0.543669   \n",
       "                                          mahalanobis                                   0.428803   \n",
       "                                          dtw                                           0.451673   \n",
       "                                          ssa                                           0.296797   \n",
       "                                          edrs                                          0.307079   \n",
       "\n",
       "                                                                                                  \\\n",
       "                                                                                            mean   \n",
       "algorithm                optim_param_name optim_param_value                                        \n",
       "Random Black Forest (RR)                  n_estimators=200,bootstrap=False,n_trees=10   0.903985   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=10    0.890477   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=10    0.890186   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=100    0.889673   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=200    0.889138   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=10   0.888855   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=10     0.888069   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=10    0.881868   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=100   0.881856   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=200   0.836222   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200        NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200        NaN   \n",
       "ARIMA                    distance_metric  twed                                          0.824566   \n",
       "                                          euclidean                                     0.822603   \n",
       "                                          garch                                         0.801877   \n",
       "                                          fourier                                       0.789106   \n",
       "                                          mahalanobis                                   0.749204   \n",
       "                                          dtw                                           0.743309   \n",
       "                                          ssa                                           0.613737   \n",
       "                                          edrs                                          0.585320   \n",
       "\n",
       "                                                                                                  \\\n",
       "                                                                                          median   \n",
       "algorithm                optim_param_name optim_param_value                                        \n",
       "Random Black Forest (RR)                  n_estimators=200,bootstrap=False,n_trees=10   0.893457   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=10    0.871508   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=10    0.872921   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=100    0.871924   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=200    0.872064   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=10   0.870925   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=10     0.872081   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=10    0.870866   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=100   0.871638   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=200   0.837644   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200        NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100       NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200       NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100        NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200        NaN   \n",
       "ARIMA                    distance_metric  twed                                          0.895639   \n",
       "                                          euclidean                                     0.883404   \n",
       "                                          garch                                         0.825859   \n",
       "                                          fourier                                       0.848267   \n",
       "                                          mahalanobis                                   0.831612   \n",
       "                                          dtw                                           0.741703   \n",
       "                                          ssa                                           0.711949   \n",
       "                                          edrs                                          0.569212   \n",
       "\n",
       "                                                                                       train_main_time  \\\n",
       "                                                                                                  mean   \n",
       "algorithm                optim_param_name optim_param_value                                              \n",
       "Random Black Forest (RR)                  n_estimators=200,bootstrap=False,n_trees=10       592.900867   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=10        202.904011   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=10        371.430508   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=100        202.512872   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=200        390.331313   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=10       319.221750   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=10          29.630008   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=10         36.907738   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=100       323.007021   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=200       676.967207   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100             NaN   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200             NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100              NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200              NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100             NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200             NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100              NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200              NaN   \n",
       "ARIMA                    distance_metric  twed                                                     NaN   \n",
       "                                          euclidean                                                NaN   \n",
       "                                          garch                                                    NaN   \n",
       "                                          fourier                                                  NaN   \n",
       "                                          mahalanobis                                              NaN   \n",
       "                                          dtw                                                      NaN   \n",
       "                                          ssa                                                      NaN   \n",
       "                                          edrs                                                     NaN   \n",
       "\n",
       "                                                                                       execute_main_time  \\\n",
       "                                                                                                    mean   \n",
       "algorithm                optim_param_name optim_param_value                                                \n",
       "Random Black Forest (RR)                  n_estimators=200,bootstrap=False,n_trees=10          12.263722   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=10            9.182927   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=10           11.810795   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=100            8.796479   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=200           11.711196   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=10           9.705644   \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=10             5.924876   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=10            6.036196   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=100           9.432247   \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=200          12.128848   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100               NaN   \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200               NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100                NaN   \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200                NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100               NaN   \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200               NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100                NaN   \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200                NaN   \n",
       "ARIMA                    distance_metric  twed                                                783.581304   \n",
       "                                          euclidean                                           785.055934   \n",
       "                                          garch                                               775.989576   \n",
       "                                          fourier                                             776.005241   \n",
       "                                          mahalanobis                                         781.116264   \n",
       "                                          dtw                                                 757.913729   \n",
       "                                          ssa                                                 774.143631   \n",
       "                                          edrs                                                819.568392   \n",
       "\n",
       "                                                                                       repetition  \n",
       "                                                                                            count  \n",
       "algorithm                optim_param_name optim_param_value                                        \n",
       "Random Black Forest (RR)                  n_estimators=200,bootstrap=False,n_trees=10          10  \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=10           10  \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=10           10  \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=100           10  \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=200           10  \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=10          10  \n",
       "                                          n_estimators=10,bootstrap=True,n_trees=10            10  \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=10           10  \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=100          10  \n",
       "                                          n_estimators=10,bootstrap=False,n_trees=200          10  \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100         10  \n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200         10  \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100          10  \n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200          10  \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100         10  \n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200         10  \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100          10  \n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200          10  \n",
       "ARIMA                    distance_metric  twed                                                 11  \n",
       "                                          euclidean                                            11  \n",
       "                                          garch                                                11  \n",
       "                                          fourier                                              11  \n",
       "                                          mahalanobis                                          11  \n",
       "                                          dtw                                                  11  \n",
       "                                          ssa                                                  11  \n",
       "                                          edrs                                                 11  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_by = (\"ROC_AUC\", \"mean\")\n",
    "metric_agg_type = [\"min\", \"mean\", \"median\"]\n",
    "time_agg_type = \"mean\"\n",
    "aggs = {\n",
    "    \"PR_AUC\": metric_agg_type,\n",
    "    \"ROC_AUC\": metric_agg_type,\n",
    "    \"train_main_time\": time_agg_type,\n",
    "    \"execute_main_time\": time_agg_type,\n",
    "    \"repetition\": \"count\"\n",
    "}\n",
    "\n",
    "df_tmp = df.reset_index()\n",
    "df_tmp = df_tmp.groupby(by=[\"algorithm\", \"optim_param_name\", \"optim_param_value\"]).agg(aggs)\n",
    "df_tmp = df_tmp.reset_index()\n",
    "df_tmp = df_tmp.sort_values(by=[\"algorithm\", \"optim_param_name\", sort_by], ascending=False)\n",
    "df_tmp = df_tmp.set_index([\"algorithm\", \"optim_param_name\", \"optim_param_value\"])\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selected parameters\n",
    "\n",
    "- Random Black Forest (RR): \n",
    "  ```json\n",
    "  \"Random Black Forest (RR)\": {\n",
    "      \"bootstrap\": false,\n",
    "      \"n_trees\": 10,\n",
    "      \"n_estimators\": 200\n",
    "  }\n",
    "  ```\n",
    "- ARIMA:\n",
    "  ```json\n",
    "  \"ARIMA\": {\n",
    "      \"distance_metric\": \"twed\"\n",
    "  }\n",
    "  ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-964f728b5097>:38: UserWarning: No ROC_AUC score found! Probably Random Black Forest (RR) with params =bootstrap=False,n_trees=10,n_estimators=200 was not executed on ecg-type-variance.\n",
      "  warnings.warn(f\"No ROC_AUC score found! Probably {algo} with params {optim_params} was not executed on {dataset_name}.\")\n",
      "<ipython-input-7-964f728b5097>:38: UserWarning: No ROC_AUC score found! Probably Random Black Forest (RR) with params =bootstrap=True,n_trees=10,n_estimators=200 was not executed on ecg-type-variance.\n",
      "  warnings.warn(f\"No ROC_AUC score found! Probably {algo} with params {optim_params} was not executed on {dataset_name}.\")\n",
      "<ipython-input-7-964f728b5097>:38: UserWarning: No ROC_AUC score found! Probably Random Black Forest (RR) with params =bootstrap=False,n_trees=10,n_estimators=100 was not executed on ecg-type-variance.\n",
      "  warnings.warn(f\"No ROC_AUC score found! Probably {algo} with params {optim_params} was not executed on {dataset_name}.\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"Random Black Forest (RR)\", \"\", \"bootstrap=False,n_trees=10,n_estimators=200\"),\n",
    "    (\"Random Black Forest (RR)\", \"\", \"bootstrap=True,n_trees=10,n_estimators=200\"),\n",
    "    (\"Random Black Forest (RR)\", \"\", \"bootstrap=False,n_trees=10,n_estimators=100\")\n",
    "], \"ecg-type-variance\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Failed runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>repetition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>optim_param_name</th>\n",
       "      <th>optim_param_value</th>\n",
       "      <th>status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Random Black Forest (RR)</th>\n",
       "      <th rowspan=\"10\" valign=\"top\"></th>\n",
       "      <th>n_estimators=10,bootstrap=False,n_trees=200</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=False,n_trees=100</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=False,n_trees=200</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=True,n_trees=100</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=100,bootstrap=True,n_trees=200</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=False,n_trees=10</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=False,n_trees=100</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=False,n_trees=200</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=True,n_trees=100</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators=200,bootstrap=True,n_trees=200</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                     repetition\n",
       "algorithm                optim_param_name optim_param_value                            status                  \n",
       "Random Black Forest (RR)                  n_estimators=10,bootstrap=False,n_trees=200  Status.ERROR           7\n",
       "                                          n_estimators=100,bootstrap=False,n_trees=100 Status.ERROR          10\n",
       "                                          n_estimators=100,bootstrap=False,n_trees=200 Status.ERROR          10\n",
       "                                          n_estimators=100,bootstrap=True,n_trees=100  Status.ERROR          10\n",
       "                                          n_estimators=100,bootstrap=True,n_trees=200  Status.ERROR          10\n",
       "                                          n_estimators=200,bootstrap=False,n_trees=10  Status.ERROR           3\n",
       "                                          n_estimators=200,bootstrap=False,n_trees=100 Status.ERROR          10\n",
       "                                          n_estimators=200,bootstrap=False,n_trees=200 Status.ERROR          10\n",
       "                                          n_estimators=200,bootstrap=True,n_trees=100  Status.ERROR          10\n",
       "                                          n_estimators=200,bootstrap=True,n_trees=200  Status.ERROR          10"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"status\"] != \"Status.OK\"].groupby(by=[\"algorithm\", \"optim_param_name\", \"optim_param_value\", \"status\"])[[\"repetition\"]].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90\n"
     ]
    }
   ],
   "source": [
    "algo = \"Random Black Forest (RR)\"\n",
    "executions = [f for f in (result_path / algo).glob(\"**/execution.log\") if not (f.parent / \"anomaly_scores.ts\").is_file()]\n",
    "c = 0\n",
    "for x in executions:\n",
    "    with x.open() as fh:\n",
    "        log = \"\".join(fh.readlines())\n",
    "    if \"status code '137'\" in log:\n",
    "        c += 1\n",
    "    else:\n",
    "        print(x.parent.parent.name)\n",
    "        print(\"---------------------------------------------------------------------------------\")\n",
    "        print(log)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval38",
   "language": "python",
   "name": "timeeval38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
